Neste estudo, testamos uma fertirrigação de efluente suíno que afeta a fauna aquática subterrânea do aqüífero livre poroso. Os parâmetros químicos e químicos das águas subterrâneas foram determinados e correlacionados com a fauna que não apresentam fator aquático em áreas fertigadas e não fertigadas com efluentes que não são biodigestores. Como influências da sazonalidade na qualidade da água também foram testadas. Para esse fim, como águas subterrâneas de poços de água e piezômetros preexistentes de propriedade de agricultores, usando o ventilador e a rede de malha de 65 micra para filtrar os organismos. Os resultados físicos e químicos podem mostrar algumas alterações nos parâmetros de qualidade.Registramos doze táxons de invertebrados, sendo Acari e Copepoda os mais prevalentes. A colonização de espécies aquáticas pode ter sido limitada pelo surgimento de organismos exóticos e pela qualidade da água.

Branchoneta is an option as live food for aquaculture and it is necessary to develop more studies to get information that makes possible its culture on a large scale. Our aim was to establish a method that results in a higher percentage hatching of D. brasiliensis. We analyzed if the cyst density could cause any significant difference in hatching; for that test we use 2 different conditions: (I) 15 ml glass tube (T1, T2, and T3) with 25 (twenty-five) cysts/repetition; and (II)  Erlenmeyer of 150 ml (T4, T5, and T6) with 25 cysts/repetition totaling 75 cysts/treatment, with triplicates to all treatments. We also tested 3 different pH conditions: acid (pH 3), neutral (pH~7/distilled water) and alkaline (pH 8), all in natural light and temperature. We conclude that there is no difference between the treatments, for none of the conditions tested. But other results have to be considered as Hatching Speed index and the Average Hatching Time with best results for the treatments T3, T1, and T2, respectively. The density of 0.6 ml/cyst (glass tube) resulted in faster hatching, which shows the necessity of further studies to analyze the speed of hatching under different conditions of density. The relative frequency showed that the hatchings peak occurs in the second day. We conclude that pH and density, in this study, not influenced the beginning of the hatching process.

The Dendrocephalus brasiliensis, a native species from South America, is a freshwater crustacean well explored in conservational and productive activities. Its main characteristics are its rusticity and resistance cysts production, in which the hatching requires a period of dehydration. Independent of the species utilization nature, it is essential to manipulate its cysts, such as the counting using microscopes. Manually counting is a difficult task, prone to errors, and that also very time-consuming. In this paper, we propose an automatized approach for the detection and counting of Dendrocephalus brasiliensis cysts from images captured by a digital microscope. For this purpose, we built the DBrasiliensis dataset, a repository with 246 images containing 5141 cysts of Dendrocephalus brasiliensis. Then, we trained two state-of-the-art object detection methods, YOLOv3 (You Only Look Once) and Faster R-CNN (Region-based Convolutional Neural Networks), on DBrasiliensis dataset in order to compare them under both cyst detection and counting tasks. Experiments showed evidence that YOLOv3 is superior to Faster R-CNN, achieving an accuracy rate of 83,74%, R2 of 0.88, RMSE (Root Mean Square Error) of 3.49, and MAE (Mean Absolute Error) of 2.24 on cyst detection and counting. Moreover, we showed that is possible to infer the number of cysts of a substrate, with known weight, by performing the automated counting of some of its samples. In conclusion, the proposed approach using YOLOv3 is adequate to detect and count Dendrocephalus brasiliensis cysts. The DBrasiliensis dataset can be accessed at: https://doi.org/10.6084/m9.figshare.13073240.